Thank you for this work 💯
Could you please add a english documentation for the technical part (how to install, build, run, etc.)
Thank you very much 👍

in file model.py, _build_var_ema create and var_ema, and apply to all trainable variables, but it didn't use average to update the trainable variables. only in graph_handler.py, when load model from save would use var_ema。
is this a trick?
when training, create shadow variables for trainable variables, but dont update them, when predict, use this to initial the variables .
if it is, then

As Simple Transformers grows, the single page README documentation has gotten quite bloated and difficult to use. Because of this, I've decided that it's time (if not a little late already) to move the documentation to a more user-friendly Github Pages hosted website at the link below.

https://thilinarajapakse.github.io/simpletransformers/

As of now, only the text classification section is

prediction should include a hyper link to the answer. On clicking the answer from UI will open the pdf page where the answer/paragraph

When using a Finder with a TfidfRetriever (InMemoryDocumentStore) and default TransformersReader all indices and scores are printed (see line 75 in tfidf.py), and there is no meta-data being inserted into the documents which are returned (line 96). I commented out the print call and added the following line to the Document constructor:

meta={'name':self.document_store.get_document_by_id(

$print "XXX"$ and $print("XXX")$ are all used in the program. I think it is better to keep consistent in python 2.7 grammar or python 3.5 grammar.